Overview

Dataset statistics

Number of variables39
Number of observations1620
Missing cells421
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory493.7 KiB
Average record size in memory312.1 B

Variable types

Numeric28
Categorical11

Alerts

Bus is highly correlated with 상권타입 and 15 other fieldsHigh correlation
총_생활인구 is highly correlated with Bus and 16 other fieldsHigh correlation
남성_생활인구 is highly correlated with Bus and 17 other fieldsHigh correlation
여성_생활인구 is highly correlated with Bus and 15 other fieldsHigh correlation
연령대_10_생활인구 is highly correlated with 총_생활인구 and 14 other fieldsHigh correlation
연령대_20_생활인구 is highly correlated with Bus and 15 other fieldsHigh correlation
연령대_30_생활인구 is highly correlated with Bus and 17 other fieldsHigh correlation
연령대_40_생활인구 is highly correlated with Bus and 18 other fieldsHigh correlation
연령대_50_생활인구 is highly correlated with Bus and 15 other fieldsHigh correlation
연령대_60_이상_생활인구 is highly correlated with 총_생활인구 and 14 other fieldsHigh correlation
시간대_1_생활인구_수 is highly correlated with 총_생활인구 and 14 other fieldsHigh correlation
시간대_2_생활인구_수 is highly correlated with Bus and 15 other fieldsHigh correlation
시간대_3_생활인구_수 is highly correlated with 상권타입 and 19 other fieldsHigh correlation
시간대_4_생활인구_수 is highly correlated with Bus and 18 other fieldsHigh correlation
시간대_5_생활인구_수 is highly correlated with Bus and 18 other fieldsHigh correlation
시간대_6_생활인구_수 is highly correlated with 총_생활인구 and 14 other fieldsHigh correlation
아파트_단지수 is highly correlated with 총_생활인구 and 9 other fieldsHigh correlation
매출액 is highly correlated with Bus and 9 other fieldsHigh correlation
매출건수 is highly correlated with 상권타입 and 11 other fieldsHigh correlation
개업율 is highly correlated with 폐업율 and 1 other fieldsHigh correlation
폐업율 is highly correlated with 개업율 and 1 other fieldsHigh correlation
전체범죄 is highly correlated with 살인 and 7 other fieldsHigh correlation
폭력 is highly correlated with 살인 and 8 other fieldsHigh correlation
절도 is highly correlated with 살인 and 7 other fieldsHigh correlation
강간 is highly correlated with 살인 and 8 other fieldsHigh correlation
df_index is highly correlated with 상권타입High correlation
상권타입 is highly correlated with df_index and 4 other fieldsHigh correlation
Subway is highly correlated with 상권타입 and 8 other fieldsHigh correlation
유흥업소 is highly correlated with Bus and 6 other fieldsHigh correlation
살인 is highly correlated with 강간 and 5 other fieldsHigh correlation
강도 is highly correlated with 강간 and 5 other fieldsHigh correlation
방화 is highly correlated with 강간 and 3 other fieldsHigh correlation
마약 is highly correlated with 살인 and 5 other fieldsHigh correlation
약취 is highly correlated with 112신고High correlation
도박 is highly correlated with 강간 and 3 other fieldsHigh correlation
112신고 is highly correlated with 살인 and 4 other fieldsHigh correlation
프랜차이즈_침투율 is highly correlated with 개업율 and 1 other fieldsHigh correlation
아파트_단지수 has 176 (10.9%) missing values Missing
아파트_평균_시가 has 176 (10.9%) missing values Missing
매출액 has 18 (1.1%) missing values Missing
매출건수 has 18 (1.1%) missing values Missing
df_index is uniformly distributed Uniform
df_index has unique values Unique
Bus has 35 (2.2%) zeros Zeros
Subway has 334 (20.6%) zeros Zeros
유흥업소 has 1046 (64.6%) zeros Zeros
개업율 has 40 (2.5%) zeros Zeros
폐업율 has 46 (2.8%) zeros Zeros
프랜차이즈_침투율 has 77 (4.8%) zeros Zeros

Reproduction

Analysis started2022-10-16 05:51:46.089847
Analysis finished2022-10-16 05:53:44.885245
Duration1 minute and 58.8 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1620
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean829.1493827
Minimum0
Maximum1670
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:44.973419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile81.95
Q1412.75
median828.5
Q31242.25
95-th percentile1583.05
Maximum1670
Range1670
Interquartile range (IQR)829.5

Descriptive statistics

Standard deviation480.6055462
Coefficient of variation (CV)0.5796368619
Kurtosis-1.193601034
Mean829.1493827
Median Absolute Deviation (MAD)415
Skewness0.006187635767
Sum1343222
Variance230981.6911
MonotonicityStrictly increasing
2022-10-16T14:53:45.097461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
11051
 
0.1%
11151
 
0.1%
11141
 
0.1%
11131
 
0.1%
11121
 
0.1%
11111
 
0.1%
11101
 
0.1%
11091
 
0.1%
11081
 
0.1%
Other values (1610)1610
99.4%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
16701
0.1%
16691
0.1%
16681
0.1%
16671
0.1%
16661
0.1%
16651
0.1%
16641
0.1%
16631
0.1%
16621
0.1%
16611
0.1%

상권타입
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
골목상권
1062 
전통시장
303 
발달상권
249 
관광특구
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters6480
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row골목상권
2nd row골목상권
3rd row골목상권
4th row골목상권
5th row골목상권

Common Values

ValueCountFrequency (%)
골목상권1062
65.6%
전통시장303
 
18.7%
발달상권249
 
15.4%
관광특구6
 
0.4%

Length

2022-10-16T14:53:45.205154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:46.435825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
골목상권1062
65.6%
전통시장303
 
18.7%
발달상권249
 
15.4%
관광특구6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1311
20.2%
1311
20.2%
1062
16.4%
1062
16.4%
303
 
4.7%
303
 
4.7%
303
 
4.7%
303
 
4.7%
249
 
3.8%
249
 
3.8%
Other values (4)24
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter6480
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1311
20.2%
1311
20.2%
1062
16.4%
1062
16.4%
303
 
4.7%
303
 
4.7%
303
 
4.7%
303
 
4.7%
249
 
3.8%
249
 
3.8%
Other values (4)24
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul6480
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1311
20.2%
1311
20.2%
1062
16.4%
1062
16.4%
303
 
4.7%
303
 
4.7%
303
 
4.7%
303
 
4.7%
249
 
3.8%
249
 
3.8%
Other values (4)24
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul6480
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1311
20.2%
1311
20.2%
1062
16.4%
1062
16.4%
303
 
4.7%
303
 
4.7%
303
 
4.7%
303
 
4.7%
249
 
3.8%
249
 
3.8%
Other values (4)24
 
0.4%

Bus
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct43
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.512962963
Minimum0
Maximum92
Zeros35
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:46.608813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q312
95-th percentile21
Maximum92
Range92
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.940438732
Coefficient of variation (CV)0.729576974
Kurtosis17.04579781
Mean9.512962963
Median Absolute Deviation (MAD)4
Skewness2.556898029
Sum15411
Variance48.16968979
MonotonicityNot monotonic
2022-10-16T14:53:46.865689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
8142
 
8.8%
6129
 
8.0%
5120
 
7.4%
4113
 
7.0%
7107
 
6.6%
996
 
5.9%
291
 
5.6%
391
 
5.6%
1290
 
5.6%
1088
 
5.4%
Other values (33)553
34.1%
ValueCountFrequency (%)
035
 
2.2%
132
 
2.0%
291
5.6%
391
5.6%
4113
7.0%
5120
7.4%
6129
8.0%
7107
6.6%
8142
8.8%
996
5.9%
ValueCountFrequency (%)
921
 
0.1%
591
 
0.1%
512
0.1%
461
 
0.1%
452
0.1%
431
 
0.1%
412
0.1%
361
 
0.1%
341
 
0.1%
334
0.2%

Subway
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.808024691
Minimum0
Maximum19
Zeros334
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:47.027823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile5
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.754073293
Coefficient of variation (CV)0.9701600325
Kurtosis10.4468387
Mean1.808024691
Median Absolute Deviation (MAD)1
Skewness2.19477742
Sum2929
Variance3.076773119
MonotonicityNot monotonic
2022-10-16T14:53:47.218368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1531
32.8%
2343
21.2%
0334
20.6%
3194
 
12.0%
4105
 
6.5%
553
 
3.3%
626
 
1.6%
717
 
1.0%
88
 
0.5%
93
 
0.2%
Other values (5)6
 
0.4%
ValueCountFrequency (%)
0334
20.6%
1531
32.8%
2343
21.2%
3194
 
12.0%
4105
 
6.5%
553
 
3.3%
626
 
1.6%
717
 
1.0%
88
 
0.5%
93
 
0.2%
ValueCountFrequency (%)
191
 
0.1%
151
 
0.1%
131
 
0.1%
121
 
0.1%
102
 
0.1%
93
 
0.2%
88
 
0.5%
717
 
1.0%
626
1.6%
553
3.3%

평균매매가
Real number (ℝ≥0)

Distinct1593
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1102.370537
Minimum150.225338
Maximum9449.120946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:47.421326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum150.225338
5-th percentile472.6190675
Q1747.5587922
median949.7827119
Q31322.744612
95-th percentile2148.995096
Maximum9449.120946
Range9298.895608
Interquartile range (IQR)575.1858203

Descriptive statistics

Standard deviation577.0169029
Coefficient of variation (CV)0.5234328057
Kurtosis34.34576166
Mean1102.370537
Median Absolute Deviation (MAD)250.3273608
Skewness3.537881392
Sum1785840.269
Variance332948.5062
MonotonicityNot monotonic
2022-10-16T14:53:47.609964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
725.0902543
 
0.2%
1975.2475253
 
0.2%
2213.9397282
 
0.1%
756.08151572
 
0.1%
912.55790242
 
0.1%
1247.1606792
 
0.1%
423.15081952
 
0.1%
2893.4685272
 
0.1%
1186.2641362
 
0.1%
1976.7306682
 
0.1%
Other values (1583)1598
98.6%
ValueCountFrequency (%)
150.2253381
0.1%
240.86983681
0.1%
242.50788641
0.1%
249.8411951
0.1%
280.45959891
0.1%
294.67508281
0.1%
295.11445791
0.1%
296.72393621
0.1%
307.00159231
0.1%
309.93923211
0.1%
ValueCountFrequency (%)
9449.1209461
0.1%
6146.6166862
0.1%
3753.0493531
0.1%
3491.0824681
0.1%
3460.6762161
0.1%
3404.1590491
0.1%
3371.1059661
0.1%
3094.785111
0.1%
3054.1554241
0.1%
2911.796821
0.1%

유흥업소
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.011111111
Minimum0
Maximum33
Zeros1046
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:47.829840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.315962281
Coefficient of variation (CV)2.290512146
Kurtosis43.31530262
Mean1.011111111
Median Absolute Deviation (MAD)0
Skewness5.203117055
Sum1638
Variance5.363681285
MonotonicityNot monotonic
2022-10-16T14:53:48.001742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
01046
64.6%
1245
 
15.1%
2124
 
7.7%
363
 
3.9%
450
 
3.1%
528
 
1.7%
719
 
1.2%
613
 
0.8%
88
 
0.5%
95
 
0.3%
Other values (11)19
 
1.2%
ValueCountFrequency (%)
01046
64.6%
1245
 
15.1%
2124
 
7.7%
363
 
3.9%
450
 
3.1%
528
 
1.7%
613
 
0.8%
719
 
1.2%
88
 
0.5%
95
 
0.3%
ValueCountFrequency (%)
331
 
0.1%
251
 
0.1%
221
 
0.1%
182
0.1%
171
 
0.1%
161
 
0.1%
143
0.2%
133
0.2%
122
0.1%
112
0.1%

살인
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
597 
5
324 
2
282 
3
254 
4
163 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row1
4th row2
5th row5

Common Values

ValueCountFrequency (%)
1597
36.9%
5324
20.0%
2282
17.4%
3254
15.7%
4163
 
10.1%

Length

2022-10-16T14:53:48.187125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:48.405982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1597
36.9%
5324
20.0%
2282
17.4%
3254
15.7%
4163
 
10.1%

Most occurring characters

ValueCountFrequency (%)
1597
36.9%
5324
20.0%
2282
17.4%
3254
15.7%
4163
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1597
36.9%
5324
20.0%
2282
17.4%
3254
15.7%
4163
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1597
36.9%
5324
20.0%
2282
17.4%
3254
15.7%
4163
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1597
36.9%
5324
20.0%
2282
17.4%
3254
15.7%
4163
 
10.1%

강도
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
645 
5
335 
2
326 
3
260 
4
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row5
5th row1

Common Values

ValueCountFrequency (%)
1645
39.8%
5335
20.7%
2326
20.1%
3260
16.0%
454
 
3.3%

Length

2022-10-16T14:53:48.557115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:48.705639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1645
39.8%
5335
20.7%
2326
20.1%
3260
16.0%
454
 
3.3%

Most occurring characters

ValueCountFrequency (%)
1645
39.8%
5335
20.7%
2326
20.1%
3260
16.0%
454
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1645
39.8%
5335
20.7%
2326
20.1%
3260
16.0%
454
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1645
39.8%
5335
20.7%
2326
20.1%
3260
16.0%
454
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1645
39.8%
5335
20.7%
2326
20.1%
3260
16.0%
454
 
3.3%

강간
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
674 
2
378 
5
312 
3
197 
4
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row5

Common Values

ValueCountFrequency (%)
1674
41.6%
2378
23.3%
5312
19.3%
3197
 
12.2%
459
 
3.6%

Length

2022-10-16T14:53:48.832677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:48.982985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1674
41.6%
2378
23.3%
5312
19.3%
3197
 
12.2%
459
 
3.6%

Most occurring characters

ValueCountFrequency (%)
1674
41.6%
2378
23.3%
5312
19.3%
3197
 
12.2%
459
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1674
41.6%
2378
23.3%
5312
19.3%
3197
 
12.2%
459
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1674
41.6%
2378
23.3%
5312
19.3%
3197
 
12.2%
459
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1674
41.6%
2378
23.3%
5312
19.3%
3197
 
12.2%
459
 
3.6%

절도
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
516 
5
353 
3
327 
2
305 
4
119 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1516
31.9%
5353
21.8%
3327
20.2%
2305
18.8%
4119
 
7.3%

Length

2022-10-16T14:53:49.123953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:49.260479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1516
31.9%
5353
21.8%
3327
20.2%
2305
18.8%
4119
 
7.3%

Most occurring characters

ValueCountFrequency (%)
1516
31.9%
5353
21.8%
3327
20.2%
2305
18.8%
4119
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1516
31.9%
5353
21.8%
3327
20.2%
2305
18.8%
4119
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1516
31.9%
5353
21.8%
3327
20.2%
2305
18.8%
4119
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1516
31.9%
5353
21.8%
3327
20.2%
2305
18.8%
4119
 
7.3%

폭력
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
425 
2
395 
5
361 
3
241 
4
198 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row5
4th row4
5th row5

Common Values

ValueCountFrequency (%)
1425
26.2%
2395
24.4%
5361
22.3%
3241
14.9%
4198
12.2%

Length

2022-10-16T14:53:49.418638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:49.564821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1425
26.2%
2395
24.4%
5361
22.3%
3241
14.9%
4198
12.2%

Most occurring characters

ValueCountFrequency (%)
1425
26.2%
2395
24.4%
5361
22.3%
3241
14.9%
4198
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1425
26.2%
2395
24.4%
5361
22.3%
3241
14.9%
4198
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1425
26.2%
2395
24.4%
5361
22.3%
3241
14.9%
4198
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1425
26.2%
2395
24.4%
5361
22.3%
3241
14.9%
4198
12.2%

방화
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
466 
2
410 
5
310 
3
288 
4
146 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row5
4th row3
5th row4

Common Values

ValueCountFrequency (%)
1466
28.8%
2410
25.3%
5310
19.1%
3288
17.8%
4146
 
9.0%

Length

2022-10-16T14:53:49.725784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:49.872104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1466
28.8%
2410
25.3%
5310
19.1%
3288
17.8%
4146
 
9.0%

Most occurring characters

ValueCountFrequency (%)
1466
28.8%
2410
25.3%
5310
19.1%
3288
17.8%
4146
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1466
28.8%
2410
25.3%
5310
19.1%
3288
17.8%
4146
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1466
28.8%
2410
25.3%
5310
19.1%
3288
17.8%
4146
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1466
28.8%
2410
25.3%
5310
19.1%
3288
17.8%
4146
 
9.0%

마약
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
611 
5
367 
2
363 
3
181 
4
98 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row5

Common Values

ValueCountFrequency (%)
1611
37.7%
5367
22.7%
2363
22.4%
3181
 
11.2%
498
 
6.0%

Length

2022-10-16T14:53:50.042372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:50.199821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1611
37.7%
5367
22.7%
2363
22.4%
3181
 
11.2%
498
 
6.0%

Most occurring characters

ValueCountFrequency (%)
1611
37.7%
5367
22.7%
2363
22.4%
3181
 
11.2%
498
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1611
37.7%
5367
22.7%
2363
22.4%
3181
 
11.2%
498
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1611
37.7%
5367
22.7%
2363
22.4%
3181
 
11.2%
498
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1611
37.7%
5367
22.7%
2363
22.4%
3181
 
11.2%
498
 
6.0%

약취
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
922 
5
375 
2
133 
3
121 
4
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row4
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1922
56.9%
5375
23.1%
2133
 
8.2%
3121
 
7.5%
469
 
4.3%

Length

2022-10-16T14:53:50.338084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:50.491370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1922
56.9%
5375
23.1%
2133
 
8.2%
3121
 
7.5%
469
 
4.3%

Most occurring characters

ValueCountFrequency (%)
1922
56.9%
5375
23.1%
2133
 
8.2%
3121
 
7.5%
469
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1922
56.9%
5375
23.1%
2133
 
8.2%
3121
 
7.5%
469
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1922
56.9%
5375
23.1%
2133
 
8.2%
3121
 
7.5%
469
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1922
56.9%
5375
23.1%
2133
 
8.2%
3121
 
7.5%
469
 
4.3%

도박
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
627 
5
342 
2
328 
3
200 
4
123 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row4
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1627
38.7%
5342
21.1%
2328
20.2%
3200
 
12.3%
4123
 
7.6%

Length

2022-10-16T14:53:50.639384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:50.800717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1627
38.7%
5342
21.1%
2328
20.2%
3200
 
12.3%
4123
 
7.6%

Most occurring characters

ValueCountFrequency (%)
1627
38.7%
5342
21.1%
2328
20.2%
3200
 
12.3%
4123
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1627
38.7%
5342
21.1%
2328
20.2%
3200
 
12.3%
4123
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1627
38.7%
5342
21.1%
2328
20.2%
3200
 
12.3%
4123
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1627
38.7%
5342
21.1%
2328
20.2%
3200
 
12.3%
4123
 
7.6%

전체범죄
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
470 
2
358 
5
329 
3
292 
4
171 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row5
4th row4
5th row4

Common Values

ValueCountFrequency (%)
1470
29.0%
2358
22.1%
5329
20.3%
3292
18.0%
4171
 
10.6%

Length

2022-10-16T14:53:50.975737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T14:53:51.161526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1470
29.0%
2358
22.1%
5329
20.3%
3292
18.0%
4171
 
10.6%

Most occurring characters

ValueCountFrequency (%)
1470
29.0%
2358
22.1%
5329
20.3%
3292
18.0%
4171
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1470
29.0%
2358
22.1%
5329
20.3%
3292
18.0%
4171
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1470
29.0%
2358
22.1%
5329
20.3%
3292
18.0%
4171
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1470
29.0%
2358
22.1%
5329
20.3%
3292
18.0%
4171
 
10.6%

112신고
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86836.67593
Minimum28589
Maximum139303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:51.357058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum28589
5-th percentile33232
Q168076
median90549
Q3114534
95-th percentile132570
Maximum139303
Range110714
Interquartile range (IQR)46458

Descriptive statistics

Standard deviation31435.57037
Coefficient of variation (CV)0.3620079884
Kurtosis-0.8955909093
Mean86836.67593
Median Absolute Deviation (MAD)23985
Skewness-0.1638955345
Sum140675415
Variance988195084.5
MonotonicityNot monotonic
2022-10-16T14:53:51.539118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13257092
 
5.7%
11453480
 
4.9%
11468877
 
4.8%
12938273
 
4.5%
9254572
 
4.4%
13930365
 
4.0%
9054964
 
4.0%
7976563
 
3.9%
12592962
 
3.8%
9637461
 
3.8%
Other values (21)911
56.2%
ValueCountFrequency (%)
2858935
2.2%
2940631
1.9%
3323250
3.1%
3751236
2.2%
3924128
1.7%
4442736
2.2%
4524034
2.1%
4593547
2.9%
5207935
2.2%
5993345
2.8%
ValueCountFrequency (%)
13930365
4.0%
13257092
5.7%
12938273
4.5%
12592962
3.8%
11468877
4.8%
11453480
4.9%
10025458
3.6%
9790735
 
2.2%
9637461
3.8%
9596661
3.8%

총_생활인구
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1613
Distinct (%)99.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3002876.968
Minimum21
Maximum24284228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:51.724641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile162610.8
Q1848852
median2102266
Q34176310
95-th percentile8735136.3
Maximum24284228
Range24284207
Interquartile range (IQR)3327458

Descriptive statistics

Standard deviation3057567.531
Coefficient of variation (CV)1.018212722
Kurtosis6.147206356
Mean3002876.968
Median Absolute Deviation (MAD)1470329
Skewness2.066172623
Sum4861657812
Variance9.348719206 × 1012
MonotonicityNot monotonic
2022-10-16T14:53:51.894912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2975684
 
0.2%
1983953
 
0.2%
888682
 
0.1%
38658931
 
0.1%
20862451
 
0.1%
29232431
 
0.1%
24485891
 
0.1%
2905931
 
0.1%
46433711
 
0.1%
13713791
 
0.1%
Other values (1603)1603
99.0%
ValueCountFrequency (%)
211
0.1%
31411
0.1%
118841
0.1%
189891
0.1%
226691
0.1%
239071
0.1%
311681
0.1%
351371
0.1%
381861
0.1%
395201
0.1%
ValueCountFrequency (%)
242842281
0.1%
227199271
0.1%
204916071
0.1%
187551351
0.1%
182897311
0.1%
176709901
0.1%
171609401
0.1%
161847411
0.1%
157197621
0.1%
155668291
0.1%

남성_생활인구
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1612
Distinct (%)99.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1429982.848
Minimum9
Maximum12255007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:52.091143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile77821.5
Q1403737
median991167
Q31989078
95-th percentile4104929.7
Maximum12255007
Range12254998
Interquartile range (IQR)1585341

Descriptive statistics

Standard deviation1466704.381
Coefficient of variation (CV)1.025679701
Kurtosis7.15970969
Mean1429982.848
Median Absolute Deviation (MAD)691511
Skewness2.178000552
Sum2315142231
Variance2.15122174 × 1012
MonotonicityNot monotonic
2022-10-16T14:53:52.295018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1433604
 
0.2%
985803
 
0.2%
482542
 
0.1%
4495102
 
0.1%
17364971
 
0.1%
13596781
 
0.1%
9537881
 
0.1%
13577081
 
0.1%
10671011
 
0.1%
1482091
 
0.1%
Other values (1602)1602
98.9%
ValueCountFrequency (%)
91
0.1%
24181
0.1%
59911
0.1%
84181
0.1%
98411
0.1%
111741
0.1%
146581
0.1%
173681
0.1%
176711
0.1%
190551
0.1%
ValueCountFrequency (%)
122550071
0.1%
109083281
0.1%
108351501
0.1%
94518941
0.1%
92355701
0.1%
86309511
0.1%
81042921
0.1%
79316961
0.1%
77838061
0.1%
75165221
0.1%

여성_생활인구
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1612
Distinct (%)99.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1572894.191
Minimum9
Maximum12029223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:52.484239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile85783.2
Q1441710
median1084293
Q32178429
95-th percentile4729637.9
Maximum12029223
Range12029214
Interquartile range (IQR)1736719

Descriptive statistics

Standard deviation1602085.13
Coefficient of variation (CV)1.018558743
Kurtosis5.796945594
Mean1572894.191
Median Absolute Deviation (MAD)765617
Skewness2.024287028
Sum2546515695
Variance2.566676765 × 1012
MonotonicityNot monotonic
2022-10-16T14:53:52.688519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1542084
 
0.2%
998153
 
0.2%
406162
 
0.1%
13378372
 
0.1%
19835901
 
0.1%
20083511
 
0.1%
11324561
 
0.1%
15655361
 
0.1%
13814881
 
0.1%
1423841
 
0.1%
Other values (1602)1602
98.9%
ValueCountFrequency (%)
91
0.1%
7231
0.1%
58961
0.1%
105711
0.1%
127311
0.1%
128301
0.1%
160841
0.1%
165071
0.1%
208171
0.1%
218521
0.1%
ValueCountFrequency (%)
120292231
0.1%
118115971
0.1%
112560351
0.1%
96587781
0.1%
93032381
0.1%
92292451
0.1%
91380481
0.1%
85254521
0.1%
84699581
0.1%
83633071
0.1%

연령대_10_생활인구
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1610
Distinct (%)99.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean392429.7603
Minimum8
Maximum3390015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:52.860296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile13112.8
Q191585.5
median250292
Q3545843.5
95-th percentile1324796.3
Maximum3390015
Range3390007
Interquartile range (IQR)454258

Descriptive statistics

Standard deviation427557.8575
Coefficient of variation (CV)1.089514356
Kurtosis6.168707619
Mean392429.7603
Median Absolute Deviation (MAD)187735
Skewness2.133623616
Sum635343782
Variance1.828057215 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:53.032337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69994
 
0.2%
50463
 
0.2%
36092
 
0.1%
1993492
 
0.1%
87342
 
0.1%
115682
 
0.1%
5524551
 
0.1%
7121231
 
0.1%
393611
 
0.1%
1469821
 
0.1%
Other values (1600)1600
98.8%
ValueCountFrequency (%)
81
0.1%
6121
0.1%
16901
0.1%
22131
0.1%
25731
0.1%
31941
0.1%
33441
0.1%
35011
0.1%
36092
0.1%
36411
0.1%
ValueCountFrequency (%)
33900151
0.1%
28529611
0.1%
27166181
0.1%
26082861
0.1%
25297181
0.1%
25108501
0.1%
24753811
0.1%
23362921
0.1%
22814431
0.1%
22544511
0.1%

연령대_20_생활인구
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1612
Distinct (%)99.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean513780.7702
Minimum0
Maximum7434365
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:53.209368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23605
Q1122166.5
median315582
Q3671720.5
95-th percentile1612680.8
Maximum7434365
Range7434365
Interquartile range (IQR)549554

Descriptive statistics

Standard deviation651347.0043
Coefficient of variation (CV)1.267752789
Kurtosis23.09927451
Mean513780.7702
Median Absolute Deviation (MAD)234423
Skewness3.743143052
Sum831811067
Variance4.2425292 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:53.372716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
519824
 
0.2%
236053
 
0.2%
194032
 
0.1%
02
 
0.1%
4319151
 
0.1%
249881
 
0.1%
18888711
 
0.1%
4623421
 
0.1%
3947371
 
0.1%
5259441
 
0.1%
Other values (1602)1602
98.9%
ValueCountFrequency (%)
02
0.1%
4251
0.1%
26211
0.1%
26891
0.1%
30861
0.1%
32021
0.1%
32901
0.1%
35381
0.1%
39961
0.1%
49821
0.1%
ValueCountFrequency (%)
74343651
0.1%
67016451
0.1%
53554791
0.1%
52602261
0.1%
52157721
0.1%
47131861
0.1%
45409931
0.1%
44225521
0.1%
43302121
0.1%
39262201
0.1%

연령대_30_생활인구
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1610
Distinct (%)99.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean526534.8462
Minimum0
Maximum6203613
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:53.587396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26259.2
Q1133578.5
median343116
Q3704963.5
95-th percentile1654337.7
Maximum6203613
Range6203613
Interquartile range (IQR)571385

Descriptive statistics

Standard deviation601915.084
Coefficient of variation (CV)1.143162866
Kurtosis13.15693092
Mean526534.8462
Median Absolute Deviation (MAD)244750
Skewness2.85466437
Sum852459916
Variance3.623017684 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:53.802865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
804814
 
0.2%
361363
 
0.2%
2813032
 
0.1%
175942
 
0.1%
2243102
 
0.1%
1744442
 
0.1%
4694311
 
0.1%
10886421
 
0.1%
2464081
 
0.1%
3948521
 
0.1%
Other values (1600)1600
98.8%
ValueCountFrequency (%)
01
0.1%
61
0.1%
17121
0.1%
30951
0.1%
32241
0.1%
34201
0.1%
37441
0.1%
48821
0.1%
50051
0.1%
51241
0.1%
ValueCountFrequency (%)
62036131
0.1%
51681481
0.1%
44969751
0.1%
39628081
0.1%
39114121
0.1%
37430331
0.1%
37314111
0.1%
36611111
0.1%
35973641
0.1%
34694291
0.1%

연령대_40_생활인구
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1611
Distinct (%)99.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean481699.1476
Minimum0
Maximum4293085
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:54.014195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26783.3
Q1135671
median329880
Q3670467.5
95-th percentile1434865.6
Maximum4293085
Range4293085
Interquartile range (IQR)534796.5

Descriptive statistics

Standard deviation500696.8867
Coefficient of variation (CV)1.039439013
Kurtosis7.739455563
Mean481699.1476
Median Absolute Deviation (MAD)231946
Skewness2.258937197
Sum779870920
Variance2.506973723 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:54.220741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
635584
 
0.2%
373753
 
0.2%
4549012
 
0.1%
127322
 
0.1%
1487232
 
0.1%
4859051
 
0.1%
2875941
 
0.1%
4711871
 
0.1%
4152821
 
0.1%
568261
 
0.1%
Other values (1601)1601
98.8%
ValueCountFrequency (%)
01
0.1%
4951
0.1%
22041
0.1%
34091
0.1%
36781
0.1%
47301
0.1%
58511
0.1%
59381
0.1%
67151
0.1%
73241
0.1%
ValueCountFrequency (%)
42930851
0.1%
37982751
0.1%
34364131
0.1%
33075621
0.1%
32929521
0.1%
29823261
0.1%
29748441
0.1%
28099581
0.1%
27695191
0.1%
27283501
0.1%

연령대_50_생활인구
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1612
Distinct (%)99.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean437845.5911
Minimum0
Maximum2921527
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:54.392041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24835.9
Q1124817
median303500
Q3618799.5
95-th percentile1252785.6
Maximum2921527
Range2921527
Interquartile range (IQR)493982.5

Descriptive statistics

Standard deviation436497.5088
Coefficient of variation (CV)0.9969211012
Kurtosis4.637089668
Mean437845.5911
Median Absolute Deviation (MAD)211363
Skewness1.89194853
Sum708872012
Variance1.905300752 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:54.552391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
457444
 
0.2%
423443
 
0.2%
153132
 
0.1%
727242
 
0.1%
5389701
 
0.1%
3303971
 
0.1%
4542851
 
0.1%
2944741
 
0.1%
342591
 
0.1%
6985601
 
0.1%
Other values (1602)1602
98.9%
ValueCountFrequency (%)
01
0.1%
2851
0.1%
21281
0.1%
21961
0.1%
34031
0.1%
34271
0.1%
50471
0.1%
52701
0.1%
59221
0.1%
62411
0.1%
ValueCountFrequency (%)
29215271
0.1%
27868181
0.1%
25497491
0.1%
25395541
0.1%
25210331
0.1%
24442341
0.1%
24094151
0.1%
23973301
0.1%
23914971
0.1%
23125101
0.1%

연령대_60_이상_생활인구
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1613
Distinct (%)99.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean650587.063
Minimum8
Maximum4887130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:54.734662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile32464.8
Q1178103.5
median444904
Q3899059
95-th percentile1954866.2
Maximum4887130
Range4887122
Interquartile range (IQR)720955.5

Descriptive statistics

Standard deviation657765.0799
Coefficient of variation (CV)1.011033138
Kurtosis5.163000253
Mean650587.063
Median Absolute Deviation (MAD)317539
Skewness1.943861912
Sum1053300455
Variance4.326549003 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:54.917603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
488044
 
0.2%
538893
 
0.2%
202242
 
0.1%
6989011
 
0.1%
4349031
 
0.1%
6937651
 
0.1%
4260901
 
0.1%
337751
 
0.1%
6907741
 
0.1%
1186271
 
0.1%
Other values (1603)1603
99.0%
ValueCountFrequency (%)
81
0.1%
17431
0.1%
28411
0.1%
32401
0.1%
51611
0.1%
52631
0.1%
57761
0.1%
83481
0.1%
125031
0.1%
127181
0.1%
ValueCountFrequency (%)
48871301
0.1%
46304871
0.1%
39390221
0.1%
38922421
0.1%
38628631
0.1%
37626411
0.1%
36986461
0.1%
35690061
0.1%
35293911
0.1%
33732901
0.1%

시간대_1_생활인구_수
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1611
Distinct (%)99.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean737064.9277
Minimum0
Maximum6555148
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:55.147082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25193.1
Q1166782
median482318
Q31018847
95-th percentile2265831.8
Maximum6555148
Range6555148
Interquartile range (IQR)852065

Descriptive statistics

Standard deviation794851.1309
Coefficient of variation (CV)1.078400424
Kurtosis6.556118952
Mean737064.9277
Median Absolute Deviation (MAD)361511
Skewness2.106334224
Sum1193308118
Variance6.317883203 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:55.367342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
754654
 
0.2%
537193
 
0.2%
171282
 
0.1%
2845552
 
0.1%
02
 
0.1%
11142401
 
0.1%
365541
 
0.1%
18210231
 
0.1%
4662281
 
0.1%
6022101
 
0.1%
Other values (1601)1601
98.8%
ValueCountFrequency (%)
02
0.1%
3551
0.1%
14971
0.1%
30991
0.1%
32281
0.1%
62301
0.1%
65361
0.1%
66031
0.1%
68421
0.1%
71611
0.1%
ValueCountFrequency (%)
65551481
0.1%
63485141
0.1%
50886461
0.1%
47657691
0.1%
46887281
0.1%
46746111
0.1%
43472701
0.1%
40385311
0.1%
39728001
0.1%
39727001
0.1%

시간대_2_생활인구_수
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1612
Distinct (%)99.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean614394.9592
Minimum2
Maximum4780126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:55.547881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile31810.9
Q1172869.5
median429901
Q3870394
95-th percentile1797588.3
Maximum4780126
Range4780124
Interquartile range (IQR)697524.5

Descriptive statistics

Standard deviation622549.5146
Coefficient of variation (CV)1.013272497
Kurtosis5.974249433
Mean614394.9592
Median Absolute Deviation (MAD)298873
Skewness2.033335174
Sum994705439
Variance3.875678981 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:55.788847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
602444
 
0.2%
400793
 
0.2%
165402
 
0.1%
866872
 
0.1%
7348391
 
0.1%
7688691
 
0.1%
5671421
 
0.1%
4623561
 
0.1%
607011
 
0.1%
9315741
 
0.1%
Other values (1602)1602
98.9%
ValueCountFrequency (%)
21
0.1%
5941
0.1%
26521
0.1%
37121
0.1%
47021
0.1%
56021
0.1%
64631
0.1%
73681
0.1%
77921
0.1%
88141
0.1%
ValueCountFrequency (%)
47801261
0.1%
47377591
0.1%
42633621
0.1%
39240331
0.1%
37264351
0.1%
35394531
0.1%
32735901
0.1%
32328661
0.1%
31990721
0.1%
31807821
0.1%

시간대_3_생활인구_수
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1611
Distinct (%)99.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean380344.2823
Minimum7
Maximum4510092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:55.973413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile23964.5
Q1112321.5
median266186
Q3517312.5
95-th percentile1132706.4
Maximum4510092
Range4510085
Interquartile range (IQR)404991

Descriptive statistics

Standard deviation406376.6291
Coefficient of variation (CV)1.068444165
Kurtosis15.30026496
Mean380344.2823
Median Absolute Deviation (MAD)179115
Skewness2.922187371
Sum615777393
Variance1.651419646 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:56.164897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410594
 
0.2%
286333
 
0.2%
1428952
 
0.1%
8327952
 
0.1%
133212
 
0.1%
998991
 
0.1%
11472421
 
0.1%
3532651
 
0.1%
455831
 
0.1%
10491501
 
0.1%
Other values (1601)1601
98.8%
ValueCountFrequency (%)
71
0.1%
8461
0.1%
27441
0.1%
27561
0.1%
27641
0.1%
45841
0.1%
46371
0.1%
48661
0.1%
58391
0.1%
63481
0.1%
ValueCountFrequency (%)
45100921
0.1%
35687761
0.1%
33902741
0.1%
30976331
0.1%
26676301
0.1%
26368891
0.1%
26194571
0.1%
24662301
0.1%
23580421
0.1%
21734121
0.1%

시간대_4_생활인구_수
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1613
Distinct (%)99.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean383386.7474
Minimum4
Maximum4683331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:56.332100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile24176.9
Q1113085.5
median262772
Q3523194
95-th percentile1113356.6
Maximum4683331
Range4683327
Interquartile range (IQR)410108.5

Descriptive statistics

Standard deviation413901.9857
Coefficient of variation (CV)1.079593879
Kurtosis15.95177462
Mean383386.7474
Median Absolute Deviation (MAD)177806
Skewness3.005440615
Sum620703144
Variance1.713148538 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:56.520425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
368394
 
0.2%
259843
 
0.2%
142912
 
0.1%
6175061
 
0.1%
2824081
 
0.1%
4075061
 
0.1%
3836711
 
0.1%
454171
 
0.1%
10568691
 
0.1%
3299701
 
0.1%
Other values (1603)1603
99.0%
ValueCountFrequency (%)
41
0.1%
11641
0.1%
26211
0.1%
31391
0.1%
32071
0.1%
47091
0.1%
49291
0.1%
55591
0.1%
61791
0.1%
63951
0.1%
ValueCountFrequency (%)
46833311
0.1%
35113041
0.1%
33465561
0.1%
31273311
0.1%
28627381
0.1%
26898331
0.1%
26283521
0.1%
26266881
0.1%
24950681
0.1%
24235511
0.1%

시간대_5_생활인구_수
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1613
Distinct (%)99.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean514472.2989
Minimum2
Maximum5149006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:56.714689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile30808.6
Q1146349.5
median353935
Q3713357.5
95-th percentile1503605.7
Maximum5149006
Range5149004
Interquartile range (IQR)567008

Descriptive statistics

Standard deviation538408.3183
Coefficient of variation (CV)1.04652538
Kurtosis9.257851517
Mean514472.2989
Median Absolute Deviation (MAD)246452
Skewness2.419435261
Sum832930652
Variance2.898835172 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:56.877766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
426804
 
0.2%
269463
 
0.2%
175452
 
0.1%
8938601
 
0.1%
3881001
 
0.1%
5791871
 
0.1%
5049671
 
0.1%
512241
 
0.1%
10050291
 
0.1%
2090161
 
0.1%
Other values (1603)1603
99.0%
ValueCountFrequency (%)
21
0.1%
4861
0.1%
26181
0.1%
29091
0.1%
40701
0.1%
58571
0.1%
59421
0.1%
62381
0.1%
68201
0.1%
83071
0.1%
ValueCountFrequency (%)
51490061
0.1%
39839631
0.1%
35522051
0.1%
35422971
0.1%
34784191
0.1%
34353621
0.1%
31604471
0.1%
31438281
0.1%
30356871
0.1%
30099281
0.1%

시간대_6_생활인구_수
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1612
Distinct (%)99.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean373214.2038
Minimum0
Maximum2942666
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:57.059666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16286.4
Q196496.5
median250095
Q3513789
95-th percentile1124096.6
Maximum2942666
Range2942666
Interquartile range (IQR)417292.5

Descriptive statistics

Standard deviation385928.9451
Coefficient of variation (CV)1.034068214
Kurtosis5.618081335
Mean373214.2038
Median Absolute Deviation (MAD)184442
Skewness1.997230107
Sum604233796
Variance1.489411506 × 1011
MonotonicityNot monotonic
2022-10-16T14:53:57.302856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412814
 
0.2%
230343
 
0.2%
100442
 
0.1%
282222
 
0.1%
6633121
 
0.1%
3866821
 
0.1%
2890791
 
0.1%
319281
 
0.1%
2800481
 
0.1%
516541
 
0.1%
Other values (1602)1602
98.9%
ValueCountFrequency (%)
01
0.1%
511
0.1%
3621
0.1%
17071
0.1%
20141
0.1%
21711
0.1%
25641
0.1%
38201
0.1%
39241
0.1%
42321
0.1%
ValueCountFrequency (%)
29426661
0.1%
28756351
0.1%
25155081
0.1%
23855601
0.1%
22446511
0.1%
20843641
0.1%
20763211
0.1%
20722271
0.1%
20374051
0.1%
19437721
0.1%

아파트_단지수
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct210
Distinct (%)14.5%
Missing176
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean48.82409972
Minimum1
Maximum758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:57.517447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median29
Q366
95-th percentile163.85
Maximum758
Range757
Interquartile range (IQR)57

Descriptive statistics

Standard deviation61.49066109
Coefficient of variation (CV)1.259432564
Kurtosis18.90523382
Mean48.82409972
Median Absolute Deviation (MAD)23
Skewness3.242268195
Sum70502
Variance3781.101401
MonotonicityNot monotonic
2022-10-16T14:53:57.696412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169
 
4.3%
250
 
3.1%
447
 
2.9%
344
 
2.7%
538
 
2.3%
835
 
2.2%
1231
 
1.9%
730
 
1.9%
628
 
1.7%
1628
 
1.7%
Other values (200)1044
64.4%
(Missing)176
 
10.9%
ValueCountFrequency (%)
169
4.3%
250
3.1%
344
2.7%
447
2.9%
538
2.3%
628
1.7%
730
1.9%
835
2.2%
921
 
1.3%
1026
 
1.6%
ValueCountFrequency (%)
7581
0.1%
4241
0.1%
4201
0.1%
3882
0.1%
3811
0.1%
3691
0.1%
3612
0.1%
3551
0.1%
3321
0.1%
3291
0.1%

아파트_평균_시가
Real number (ℝ≥0)

MISSING

Distinct1444
Distinct (%)100.0%
Missing176
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean200478412.2
Minimum33142857
Maximum2334260370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:57.876129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum33142857
5-th percentile101788039.4
Q1127670256.5
median154529176.5
Q3205834809.2
95-th percentile431321653.5
Maximum2334260370
Range2301117513
Interquartile range (IQR)78164552.75

Descriptive statistics

Standard deviation165426787.9
Coefficient of variation (CV)0.825160106
Kurtosis51.28923541
Mean200478412.2
Median Absolute Deviation (MAD)33736430
Skewness5.817670427
Sum2.894908272 × 1011
Variance2.736602215 × 1016
MonotonicityNot monotonic
2022-10-16T14:53:58.059415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1327371861
 
0.1%
2415392581
 
0.1%
1245498081
 
0.1%
1175352721
 
0.1%
1220474861
 
0.1%
1630974741
 
0.1%
1635012391
 
0.1%
1824960971
 
0.1%
2148133211
 
0.1%
2182356301
 
0.1%
Other values (1434)1434
88.5%
(Missing)176
 
10.9%
ValueCountFrequency (%)
331428571
0.1%
481000001
0.1%
622618361
0.1%
655843751
0.1%
663318181
0.1%
670318881
0.1%
676471151
0.1%
691050931
0.1%
702398181
0.1%
712161141
0.1%
ValueCountFrequency (%)
23342603701
0.1%
20420069451
0.1%
20356942361
0.1%
16847587731
0.1%
14425225001
0.1%
13447145341
0.1%
11008912761
0.1%
10895824271
0.1%
10025587841
0.1%
9820152781
0.1%

개업율
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct239
Distinct (%)14.8%
Missing6
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.1073289963
Minimum0
Maximum1
Zeros40
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:58.230159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.023
Q10.073
median0.104
Q30.133
95-th percentile0.19735
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.06107443805
Coefficient of variation (CV)0.5690394969
Kurtosis34.15280973
Mean0.1073289963
Median Absolute Deviation (MAD)0.03
Skewness3.233457836
Sum173.229
Variance0.003730086983
MonotonicityNot monotonic
2022-10-16T14:53:58.405846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
040
 
2.5%
0.11425
 
1.5%
0.11124
 
1.5%
0.09922
 
1.4%
0.10521
 
1.3%
0.08321
 
1.3%
0.09320
 
1.2%
0.10319
 
1.2%
0.09518
 
1.1%
0.12318
 
1.1%
Other values (229)1386
85.6%
ValueCountFrequency (%)
040
2.5%
0.0011
 
0.1%
0.0022
 
0.1%
0.0061
 
0.1%
0.0092
 
0.1%
0.0111
 
0.1%
0.0121
 
0.1%
0.0134
 
0.2%
0.0143
 
0.2%
0.0154
 
0.2%
ValueCountFrequency (%)
11
 
0.1%
0.6351
 
0.1%
0.4841
 
0.1%
0.4551
 
0.1%
0.4091
 
0.1%
0.4061
 
0.1%
0.3781
 
0.1%
0.3751
 
0.1%
0.3334
0.2%
0.3271
 
0.1%

폐업율
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct195
Distinct (%)12.1%
Missing6
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.09715861214
Minimum0
Maximum1
Zeros46
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:58.581077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.029
Q10.07125
median0.0965
Q30.119
95-th percentile0.162
Maximum1
Range1
Interquartile range (IQR)0.04775

Descriptive statistics

Standard deviation0.0470657123
Coefficient of variation (CV)0.4844214142
Kurtosis86.77063993
Mean0.09715861214
Median Absolute Deviation (MAD)0.0235
Skewness4.881644473
Sum156.814
Variance0.002215181274
MonotonicityNot monotonic
2022-10-16T14:53:58.771549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046
 
2.8%
0.1126
 
1.6%
0.08726
 
1.6%
0.08324
 
1.5%
0.10923
 
1.4%
0.10222
 
1.4%
0.10722
 
1.4%
0.09121
 
1.3%
0.09521
 
1.3%
0.08921
 
1.3%
Other values (185)1362
84.1%
ValueCountFrequency (%)
046
2.8%
0.0121
 
0.1%
0.0131
 
0.1%
0.0141
 
0.1%
0.0162
 
0.1%
0.0181
 
0.1%
0.0192
 
0.1%
0.025
 
0.3%
0.0211
 
0.1%
0.0222
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
0.51
 
0.1%
0.2611
 
0.1%
0.2592
0.1%
0.253
0.2%
0.2341
 
0.1%
0.2311
 
0.1%
0.2291
 
0.1%
0.2221
 
0.1%
0.2191
 
0.1%

프랜차이즈_침투율
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct612
Distinct (%)37.9%
Missing6
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.3301425031
Minimum0
Maximum5.333
Zeros77
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:59.003120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.009
Q10.185
median0.297
Q30.42275
95-th percentile0.69335
Maximum5.333
Range5.333
Interquartile range (IQR)0.23775

Descriptive statistics

Standard deviation0.2592202376
Coefficient of variation (CV)0.7851768105
Kurtosis89.84114499
Mean0.3301425031
Median Absolute Deviation (MAD)0.116
Skewness5.750143332
Sum532.85
Variance0.06719513157
MonotonicityNot monotonic
2022-10-16T14:53:59.218670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
077
 
4.8%
0.26712
 
0.7%
0.411
 
0.7%
0.34811
 
0.7%
0.16711
 
0.7%
0.2569
 
0.6%
0.2478
 
0.5%
0.387
 
0.4%
0.27
 
0.4%
0.1437
 
0.4%
Other values (602)1454
89.8%
ValueCountFrequency (%)
077
4.8%
0.0041
 
0.1%
0.0081
 
0.1%
0.0093
 
0.2%
0.0121
 
0.1%
0.0131
 
0.1%
0.0141
 
0.1%
0.0161
 
0.1%
0.0211
 
0.1%
0.0221
 
0.1%
ValueCountFrequency (%)
5.3331
0.1%
1.9621
0.1%
1.8751
0.1%
1.8441
0.1%
1.7861
0.1%
1.7141
0.1%
1.61
0.1%
1.4321
0.1%
1.4121
0.1%
1.3332
0.1%

매출액
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1602
Distinct (%)100.0%
Missing18
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean5.775325232 × 1010
Minimum1036897
Maximum4.327080152 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:59.412475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1036897
5-th percentile1424166531
Q17162987833
median1.790283465 × 1010
Q34.384118393 × 1010
95-th percentile2.207859659 × 1011
Maximum4.327080152 × 1012
Range4.327079115 × 1012
Interquartile range (IQR)3.667819609 × 1010

Descriptive statistics

Standard deviation1.705476884 × 1011
Coefficient of variation (CV)2.953040419
Kurtosis275.6215531
Mean5.775325232 × 1010
Median Absolute Deviation (MAD)1.365183088 × 1010
Skewness13.35475048
Sum9.252071021 × 1013
Variance2.908651403 × 1022
MonotonicityNot monotonic
2022-10-16T14:53:59.596423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.051077151 × 10101
 
0.1%
4.221889854 × 10111
 
0.1%
2.421849532 × 10111
 
0.1%
1.272170981 × 10111
 
0.1%
1.717763057 × 10101
 
0.1%
4.683661336 × 10101
 
0.1%
1.503345229 × 10111
 
0.1%
2.152007063 × 10111
 
0.1%
6.191883251 × 10101
 
0.1%
3.3824473 × 10101
 
0.1%
Other values (1592)1592
98.3%
(Missing)18
 
1.1%
ValueCountFrequency (%)
10368971
0.1%
73928851
0.1%
513497391
0.1%
641656951
0.1%
673324521
0.1%
746873581
0.1%
790086631
0.1%
969702561
0.1%
1015220231
0.1%
1281721141
0.1%
ValueCountFrequency (%)
4.327080152 × 10121
0.1%
2.32588909 × 10121
0.1%
1.465075735 × 10121
0.1%
1.431975298 × 10121
0.1%
1.351039279 × 10121
0.1%
1.250806063 × 10121
0.1%
1.200481341 × 10121
0.1%
9.341596813 × 10111
0.1%
8.348820044 × 10111
0.1%
7.903145014 × 10111
0.1%

매출건수
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1601
Distinct (%)99.9%
Missing18
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean2190599.46
Minimum9
Maximum54328673
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-10-16T14:53:59.765155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile47665.15
Q1357115.5
median949116.5
Q32203857.75
95-th percentile8556466.35
Maximum54328673
Range54328664
Interquartile range (IQR)1846742.25

Descriptive statistics

Standard deviation4135165.371
Coefficient of variation (CV)1.887686657
Kurtosis48.26116594
Mean2190599.46
Median Absolute Deviation (MAD)706792.5
Skewness5.694422288
Sum3509340335
Variance1.709959264 × 1013
MonotonicityNot monotonic
2022-10-16T14:54:00.043093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3143582
 
0.1%
17738811
 
0.1%
67228571
 
0.1%
51534201
 
0.1%
11600321
 
0.1%
32146021
 
0.1%
106189511
 
0.1%
90015561
 
0.1%
24885731
 
0.1%
17290831
 
0.1%
Other values (1591)1591
98.2%
(Missing)18
 
1.1%
ValueCountFrequency (%)
91
0.1%
3101
0.1%
12661
0.1%
16851
0.1%
18201
0.1%
19041
0.1%
20921
0.1%
23991
0.1%
31521
0.1%
46131
0.1%
ValueCountFrequency (%)
543286731
0.1%
481488421
0.1%
468297991
0.1%
461223591
0.1%
317413531
0.1%
285991311
0.1%
252579271
0.1%
246631581
0.1%
238431951
0.1%
235894871
0.1%

Interactions

2022-10-16T14:53:38.653280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:52.794605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:55.498185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:58.178527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:00.977511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:04.047280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:07.038994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:10.451728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:13.672735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:16.967821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:20.690643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:24.828868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:29.151497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-16T14:53:17.780959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:22.964548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:27.349507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:30.847302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:34.448714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:37.778752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:41.085937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:54.936463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:57.598396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:00.388362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:03.377512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:06.416125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:09.573730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:13.038126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:16.299709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:19.988915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:23.717147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:28.383415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:32.453375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:37.149595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:41.760617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:46.242887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:50.556293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:56.098832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:00.676088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:05.049654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:09.284419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:13.504528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:17.940391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:23.088576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:27.492348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:30.979811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:34.568991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:37.894219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:41.174958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:55.053913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:57.702773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:00.484405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:03.510094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:06.522475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:09.682233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:13.127914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:16.405721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:20.084064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:23.869113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:28.501476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:32.611198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:37.282760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:41.953852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:46.367605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:50.722444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:56.235591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:00.809274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:05.196149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:09.420742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:13.618653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:18.083346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:23.278980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:27.623227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:31.120247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:34.671785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:37.990644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:41.290957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:55.153141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:57.804670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:00.584544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:03.663409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:06.611597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:09.789095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:13.238492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:16.500331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:20.187809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:24.042017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:28.653594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:32.761886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:37.426137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:42.101463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:46.512132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:50.871774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:56.385404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:00.945683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:05.349909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:09.547691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:13.816247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:18.225321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:23.388859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:27.733049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:31.288570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:34.785413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:38.152861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:41.438807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:55.241131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:57.903196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:00.686489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:03.772519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:06.708728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:10.156908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:13.349170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:16.610265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:20.305996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:24.154911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:28.778024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:32.964061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:37.561338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:42.251649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:46.670060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:51.049788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:56.544974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:01.080160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:05.514383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:09.680113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:13.972029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:18.342192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:23.484661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:27.856423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:31.412739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:34.933581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:38.267468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:41.600672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:55.329414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:51:58.002966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:00.781617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:03.868544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:06.808346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:10.249547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:13.455791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:16.728048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:20.431698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:24.464491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:28.913960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:33.112613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:37.810793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:42.385675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:46.806695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:51.208399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:56.734841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:01.244536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:05.644745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:09.814772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:14.133016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:18.467688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:23.642770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-16T14:52:13.556539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-16T14:52:20.566102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:24.646204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-16T14:52:37.997380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-16T14:52:46.933655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:51.363802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:52:56.903617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:01.377493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:05.800109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:10.003804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:14.256566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:18.578122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:23.764038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:28.093467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:31.643059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:35.149702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-16T14:53:38.548225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-16T14:54:00.264854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-16T14:54:00.800640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-16T14:54:01.175291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-16T14:54:01.532091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-16T14:54:01.802929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-16T14:53:41.917805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-16T14:53:43.432180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-16T14:53:44.045370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-16T14:53:44.611349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_index상권타입BusSubway평균매매가유흥업소살인강도강간절도폭력방화마약약취도박전체범죄112신고총_생활인구남성_생활인구여성_생활인구연령대_10_생활인구연령대_20_생활인구연령대_30_생활인구연령대_40_생활인구연령대_50_생활인구연령대_60_이상_생활인구시간대_1_생활인구_수시간대_2_생활인구_수시간대_3_생활인구_수시간대_4_생활인구_수시간대_5_생활인구_수시간대_6_생활인구_수아파트_단지수아파트_평균_시가개업율폐업율프랜차이즈_침투율매출액매출건수
00골목상권280491.19348341122551445714723720086.01736497.01983590.0552455.0431915.0469431.0566236.0603185.01096864.01114240.0734839.0375534.0371938.0593509.0530027.085.0132737186.00.0750.0850.5245.035631e+103179920.0
11골목상권61532.7214570432333252397907618511.0279750.0338763.089157.094114.086602.099050.0113757.0135835.0128621.0124120.086050.092310.0115008.072401.09.0233402400.00.0860.1141.1713.662076e+09378145.0
22골목상권190458.0686910112255144571472904553.0410933.0493621.0178301.094232.0103657.0133935.0137709.0256721.0220848.0188831.0109714.0118936.0155559.0110662.016.0130227065.00.1120.1030.3551.074877e+10422138.0
33골목상권90679.2662930252343111471472278350.0149067.0129285.046886.027093.037060.045750.053430.068137.043130.061883.045858.045585.053160.028738.07.0152415238.00.0520.0470.0403.155366e+09322021.0
44골목상권30709.914785051535453341393031338665.0613158.0725506.0201010.0197341.0261365.0214426.0190809.0273715.0357578.0271359.0151620.0154288.0222244.0181575.051.0193072717.00.0710.0860.1716.362218e+09368410.0
55골목상권122823.899053013132322221002547761815.03605602.04156214.01420558.0973285.01063921.01072662.01216311.02015072.02209269.01652457.0879240.0820749.01181860.01018236.0131.0134512690.00.1430.1350.3313.187783e+101446178.0
66골목상권301211.85814011112212132946191451537.0697410.0754128.0187079.0258989.0339852.0289759.0167628.0208234.0400328.0307246.0179472.0171933.0211107.0181448.045.0298502274.00.1500.0330.0833.376481e+09191173.0
77골목상권91928.1642070121121311137512446571.0221716.0224858.037340.078641.065948.067713.075793.0121141.0108363.097668.057645.056570.072051.054277.06.0160980467.00.0260.0770.2054.936761e+09252022.0
88골목상권56910.123069213132322221002541698505.0798440.0900066.0210915.0215002.0245016.0236920.0286637.0504016.0519370.0350875.0171547.0168989.0257069.0230656.019.0102053178.00.0650.0650.2616.313494e+09491608.0
99골목상권40890.45680803523522554959661348296.0690688.0657608.0159593.0196285.0277411.0290010.0196294.0228706.0279848.0299564.0224236.0207927.0202576.0134145.014.096404898.00.1200.0400.6401.394590e+0991770.0

Last rows

df_index상권타입BusSubway평균매매가유흥업소살인강도강간절도폭력방화마약약취도박전체범죄112신고총_생활인구남성_생활인구여성_생활인구연령대_10_생활인구연령대_20_생활인구연령대_30_생활인구연령대_40_생활인구연령대_50_생활인구연령대_60_이상_생활인구시간대_1_생활인구_수시간대_2_생활인구_수시간대_3_생활인구_수시간대_4_생활인구_수시간대_5_생활인구_수시간대_6_생활인구_수아파트_단지수아파트_평균_시가개업율폐업율프랜차이즈_침투율매출액매출건수
16101661전통시장263094.7851101553554411533232240505.0118275.0122230.09067.042494.063127.049849.035417.040548.056450.050742.034152.030988.036329.031841.0NaNNaN0.0010.0720.0091.704754e+09108695.0
16111662전통시장921007.5628230212211551252079161435.072622.088816.012366.025153.025606.023388.029396.045524.020820.030336.026157.028969.037272.017877.0NaNNaN0.0460.0990.2245.608527e+102966862.0
16121663전통시장452534.5178350333545213492545896003.0462862.0433141.039920.072562.092043.0118916.0170507.0402053.0127608.0195101.0170403.0173204.0152182.077508.04.0181350278.00.0860.0860.0002.512636e+114993885.0
16131664전통시장50802.6901970522331112292545715034.0328918.0386118.0146580.0111226.0104291.0117653.097924.0137366.0139977.0142259.0100630.0104433.0140330.087405.0NaNNaN0.1030.0820.3091.530452e+101045741.0
16141665관광특구51151530.215317611252331132858914476044.07456054.07019988.0402355.02590844.03743033.03307562.02312510.02119738.0868646.03180782.03568776.03511304.02658881.0687651.0NaNNaN0.0430.0830.2921.351039e+1248148842.0
16151666관광특구23101690.486525855255555554593512232536.05900582.06331955.0461251.02233625.03147765.02459986.01842670.02087240.02785270.02476486.01784886.01702670.01944932.01538291.011.0247400571.00.0200.0850.0638.348820e+1131741353.0
16161667관광특구59191845.992314712545511553323214806927.08104292.06702631.0440839.02446488.02793430.02809958.02786818.03529391.01711233.02960262.03097633.03127331.02815157.01095310.08.0111028306.00.0560.0830.1799.341597e+1125257927.0
16171668관광특구4581421.3145134312311131213930312340773.06005420.06335352.01145208.02752844.02964112.02019463.01571845.01887299.02215384.02345238.01849346.01971417.02605349.01354034.026.0507415555.00.1090.1160.4236.100271e+1120677421.0
16181669관광특구1331745.04995715255525155125929370377.0186040.0184337.022353.079247.0105716.081485.045218.036357.025219.072773.084961.088061.078595.020764.0NaNNaN0.0320.0810.4586.666761e+1115118145.0
16191670관광특구2232062.42019115355525315729706673032.03322573.03350463.0390420.01613165.01574071.01092169.0864926.01138283.01269161.01235679.0933239.01027830.01404785.0802336.071.0396684548.00.1050.1100.2023.444003e+1110973574.0